PDF to chunks API

{
  "items": [
    {
      "sequence": 1,
      "pageNumber": 1,
      "text": "The dominant sequence transduction models are based on complex recurrent or convolutional neural networks that include an encoder and a decoder. The best performing models also connect the encoder and decoder through an attention mechanism."
    },
    {
      "sequence": 2,
      "pageNumber": 1,
      "text": "We propose a new simple network architecture, the Transformer, based solely on attention mechanisms, dispensing with recurrence and convolutions entirely. Experiments on two machine translation tasks show these models to be superior in quality while being more parallelizable and requiring significantly less time to train."
    },
    {
      "sequence": 3,
      "pageNumber": 2,
      "text": "Recurrent models typically factor computation along the symbol positions of the input and output sequences. Aligning the positions to steps in computation time, they generate a sequence of hidden states ht, as a function of the previous hidden state ht-1 and the input for position t."
    },
    {
      "sequence": 4,
      "pageNumber": 2,
      "text": "The Transformer follows this overall architecture using stacked self-attention and point-wise, fully connected layers for both the encoder and decoder, shown in the left and right halves of Figure 1, respectively."
    },
    {
      "sequence": 5,
      "pageNumber": 3,
      "text": "An attention function can be described as mapping a query and a set of key-value pairs to an output, where the query, keys, values, and output are all vectors. The output is computed as a weighted sum of the values, where the weight assigned to each value is computed by a compatibility function of the query with the corresponding key."
    }
  ],
  "total": 42,
  "start": 1,
  "end": 5
}